通过街景图像和可解释的机器学习,研究街景环境特征对十字路口行人撞车的影响
1Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou, 510006, China.
Accident; analysis and prevention
|July 2, 2024
概括
街道景观的特点显著影响行人撞车. 这项研究使用街景图像和机器学习来识别高风险元素,帮助有针对性的安全改进.
科学领域:
- 城市规划和交通安全.
- 计算机视觉和机器学习在交通分析中的应用.
背景情况:
- 之前关于交通事故的研究集中在街道设计和土地使用上,忽视了街道景观的特征.
- 街景元素对行人撞车事故的影响仍未得到充分研究.
- 在交通安全研究中解释复杂的机器学习模型具有挑战性.
研究的目的:
- 调查街景特征与十字路口行人撞车事故之间的关系.
- 开发一个可解释的机器学习框架来分析这些关系.
- 为提高行人安全提供数据驱动的见解.
主要方法:
- 从街景图像 (SVIs) 中提取街景特征,使用语义细分和对象检测.
- 使用 eXtreme Gradient Boosting (XGBoost) 算法对行人撞车事件进行建模.
- 解释模型预测的SHapley增量扩展 (SHAP) 方法用于全球,本地和区域分析.
主要成果:
- 全球分析确定了交通量,商业用地和特定的街景元素 (道路,人,车辆) 作为重大风险因素.
- 局部分析显示,交叉路口的撞车导致因素存在空间异质性.
- 区域分析使得类似的十字路口可以被分组为量身定制的安全策略.
结论:
- 街景特征是行人与车辆碰撞的关键决定因素.
- 集成的XGBoost-SHAP框架提供了准确的预测和可解释的洞察力,对事故的原因.
- 这种方法通过提供视觉描述性和空间响应性策略来增强交通安全政策制定.
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